基于迁移的针对在线（MIMO）深度接收机的对抗性投毒攻击
Transfer-based Adversarial Poisoning Attacks for Online (MIMO-)Deep Receviers
AI总结：
本文提出一种针对在线深度接收机的基于迁移的对抗性投毒攻击方法，通过向导频注入对抗性扰动，损害接收机适应动态信道的能力，并在多种信道仿真中验证了攻击的有效性。
摘要：
最近，使用深度神经网络（DNNs）设计无线接收机（即深度接收机）因能确保复杂信道环境下的可靠通信而受到广泛关注。为了快速适应动态信道，在线学习已被采用以利用空中数据（例如，导频）更新深度接收机的权重。然而，神经模型的脆弱性和无线信道的开放性使这些系统暴露于恶意攻击中。为此，理解这些攻击方法对于鲁棒的接收机设计至关重要。在本文中，我们提出了一种针对在线接收机的基于迁移的对抗性投毒攻击方法。在不知道攻击目标的情况下，将对抗性扰动注入到导频中，从而投毒在线深度接收机，并损害其适应动态信道和非线性效应的能力。具体而言，我们的攻击方法使用在线元学习针对 Deep Soft Interference Cancellation (DeepSIC)[1]。作为一种经典的模型驱动的深度接收机，DeepSIC 将无线领域知识融入其架构中。这种整合使其能够仅用少量导频就能高效适应时变信道，并在多输入多输出（MIMO）场景中实现最佳性能。该场景中的深度接收机在无线通信领域有诸多应用，这促使我们研究针对它的攻击方法。具体而言，我们在合成线性、合成非线性、静态和 COST 2100 信道上的仿真中证明了我们攻击的有效性。仿真结果表明，所提出的投毒攻击显著降低了在线接收机在快速变化场景中的性能。
英文摘要：
Recently, the design of wireless receivers using deep neural networks (DNNs), known as deep receivers, has attracted extensive attention for ensuring reliable communication in complex channel environments. To adapt quickly to dynamic channels, online learning has been adopted to update the weights of deep receivers with over-the-air data (e.g., pilots). However, the fragility of neural models and the openness of wireless channels expose these systems to malicious attacks. To this end, understanding these attack methods is essential for robust receiver design. In this paper, we propose a transfer-based adversarial poisoning attack method for online receivers. Without knowledge of the attack target, adversarial perturbations are injected to the pilots, poisoning the online deep receiver and impairing its ability to adapt to dynamic channels and nonlinear effects. In particular, our attack method targets Deep Soft Interference Cancellation (DeepSIC)[1] using online meta-learning. As a classical model-driven deep receiver, DeepSIC incorporates wireless domain knowledge into its architecture. This integration allows it to adapt efficiently to time-varying channels with only a small number of pilots, achieving optimal performance in a multi-input and multi-output (MIMO) scenario. The deep receiver in this scenario has a number of applications in the field of wireless communication, which motivates our study of the attack methods targeting it. Specifically, we demonstrate the effectiveness of our attack in simulations on synthetic linear, synthetic nonlinear, static, and COST 2100 channels. Simulation results indicate that the proposed poisoning attack significantly reduces the performance of online receivers in rapidly changing scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
